Applying optimal model selection in principal stratification for causal inference

2012 ◽  
Vol 32 (11) ◽  
pp. 1815-1828 ◽  
Author(s):  
Lang'o Odondi ◽  
Roseanne McNamee
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Principal Stratification ◽  
Optimal Model

scholarly journals Optimizing Consistency and Coverage in Configurational Causal Modeling

2021 ◽  
pp. 004912412199555
Author(s):  
Michael Baumgartner ◽  
Mathias Ambühl
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Comparative Methods ◽  
Causal Models ◽  
Causal Modeling ◽  
Model Fit

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data [Formula: see text], so far, is a matter of repeatedly applying CCMs to [Formula: see text] while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from [Formula: see text]. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

AIChE Journal ◽  
2017 ◽  
Vol 64 (3) ◽  
pp. 822-834 ◽  
Author(s):  
Hong Zhao ◽  
Chunhui Zhao ◽  
Chengxia Yu ◽  
Eyal Dassau
Keyword(s):  
Type 1 Diabetes ◽  
Model Selection ◽  
Optimal Model ◽  
Order Model ◽  
Model Migration ◽  
Selection For ◽  
Glucose Prediction

scholarly journals A k-Nearest Neighbours Based Ensemble via Optimal Model Selection for Regression

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132095-132105 ◽  
Author(s):  
Amjad Ali ◽  
Muhammad Hamraz ◽  
Poom Kumam ◽  
Dost Muhammad Khan ◽  
Umair Khalil ◽  
...  
Keyword(s):  
Model Selection ◽  
Optimal Model ◽  
Nearest Neighbours ◽  
Selection For

scholarly journals Asymptotically optimal model selection method with right censored outcomes

Bernoulli ◽  
2004 ◽  
Vol 10 (6) ◽  
pp. 1011-1037 ◽  
Author(s):  
Sündüz Keles ◽  
Mark Van Der Laan ◽  
Sandrine Dudoit
Keyword(s):  
Model Selection ◽  
Selection Method ◽  
Optimal Model ◽  
Asymptotically Optimal ◽  
Model Selection Method

scholarly journals Principal Stratification in Causal Inference

Biometrics ◽  
2002 ◽  
Vol 58 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Constantine E. Frangakis ◽  
Donald B. Rubin
Keyword(s):  
Causal Inference ◽  
Principal Stratification

Study of Predictive Method Based on SVM Optimal Model Selection

2011 ◽  
Vol 65 ◽  
pp. 443-446
Author(s):  
Lch Gu ◽  
Zhw Ni ◽  
Zhj Wu
Keyword(s):  
Model Selection ◽  
Prediction Method ◽  
Computation Time ◽  
Kernel Matrix ◽  
Optimal Model ◽  
Model Framework ◽  
Wheat Scab ◽  
Predictive Method ◽  
Selection Of

The computation time consuming and poor efficiency of prediction exist in the model selection of traditional SVM. By studing on kernel matrix, a SVM-based prediction method for selecting the optimal model framework SVR-D1.2 was proposed with the help of the kernel matrix’s symmetry and positive definition and kernel alignment. The method was applied to the prediction of wheat scab, and comparison experiments were done with the main existing methods. The result shows the method has more efficiency and precision of prediction in the occurrence tendency of wheat scab. Meanwhile, it is simple, practicable.

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